December 2, 2025Frontiers in Plant ScienceOpen Access

Cluster segmentation and stereo vision-based apple localization algorithm for robotic harvesting

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Proposed method shows high depth estimation accuracy in complex orchard conditions, suggesting new advances in robotic harvesting.

Achieved over 91% detection accuracy and under 1% localization error in challenging environments.
Depth estimation errors ranged from 0.4% to 0.97% at distances of 800–1100 mm, confirming high spatial accuracy.
Assessment involved comparison against leading models like Faster R-CNN and YOLO, focusing on robust performance metrics.
May enable real-time robotic harvesting without heavy computational requirements or extensive training datasets.

Wang et al. (2025) studied this question.

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December 2, 2025Frontiers in Plant ScienceOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Proposed method shows high depth estimation accuracy in complex orchard conditions, suggesting new advances in robotic harvesting.

Achieved over 91% detection accuracy and under 1% localization error in challenging environments.
Depth estimation errors ranged from 0.4% to 0.97% at distances of 800–1100 mm, confirming high spatial accuracy.
Assessment involved comparison against leading models like Faster R-CNN and YOLO, focusing on robust performance metrics.
May enable real-time robotic harvesting without heavy computational requirements or extensive training datasets.

Wang et al. (2025) studied this question.

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